code: def _mp_sample_without_replacement(
      n_population: int, n_samples: int, method=None, random_state=None
  ) -> Any:
      """Sample integers without replacement.
  
      Select n_samples integers from the set [0, n_population) without
      replacement.
  
  
      Parameters
      ----------
      n_population : int
          The size of the set to sample from.
  
      n_samples : int
          The number of integer to sample.
  
      random_state : int, RandomState instance or None, default=None
          If int, random_state is the seed used by the random number generator;
          If RandomState instance, random_state is the random number generator;
          If None, the random number generator is the RandomState instance used
          by `np.random`.
  
      Returns
      -------
      out : ndarray of shape (n_samples,)
          The sampled subsets of integer. The subset of selected integer might
          not be randomized, see the method argument.
      """
      if n_population < 0:
          raise ValueError(
              "n_population should be greater than 0, got %s."
              % n_population
          )
  
      if n_samples > n_population:
          raise ValueError(
              "n_population should be greater or equal than "
              "n_samples, got n_samples > n_population (%s > %s)"
              % (n_samples, n_population)
          )
  
      rng = check_random_state(random_state)
      rng_randint = rng.randint
      # The following line of code are heavily inspired from python core,
      # more precisely of random.sample.
      selected = set()
      for i in range(n_samples):
          j = rng_randint(n_population, dtype=np.uint64)
          while j in selected:
              j = rng_randint(n_population, dtype=np.uint64)
          selected.add(j)
      return [int(x) for x in selected]
  
file_path: pycaret/pycaret/internal/patches/sklearn.py
imports: from typing import Any, Callable
  from unittest.mock import patch
  import numpy
  from sklearn.model_selection._validation import _fit_and_score, _score
  from sklearn.utils import check_random_state
  from pycaret.internal.pipeline import pipeline_predict_inverse_only
label_desc: Use image node_id 1 for calling a global function with example usage: _mp_sample_without_replacement(n_population, n_samples, method, random_state) and returns: unknown